Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine.

Autor: Singh AV; Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany., Ansari MHD; The BioRobotics Institute, Scuola Superiore Sant'Anna, Via Rinaldo Piaggio 34, Pontedera, 56025, Italy.; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Via Rinaldo Piaggio 34, Pontedera, 56025, Italy., Rosenkranz D; Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany., Maharjan RS; Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany., Kriegel FL; Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany., Gandhi K; Bosch Sensortec GmbH, Gerhard-Kindler-Straße 9, Reutlingen, 72770, Germany., Kanase A; Department of Bioengineering, Northeastern University, Boston, MA, 02215, USA., Singh R; Rajarshi Shahu College of Engineering, Pune, Maharashtra, 411033, India., Laux P; Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany., Luch A; Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany.
Jazyk: angličtina
Zdroj: Advanced healthcare materials [Adv Healthc Mater] 2020 Sep; Vol. 9 (17), pp. e1901862. Date of Electronic Publication: 2020 Jul 06.
DOI: 10.1002/adhm.201901862
Abstrakt: Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.
(© 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)
Databáze: MEDLINE
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